Semi-Parametric Models for Longitudinal Data Analysis

Semi-parametric Methods for Longitudinal Data Analysis

Self-modeling regression is a powerful semi-parametric tool for analysis of longitudinal data with time-invariant covariates. This paper explores the use of self-modelling regression for tting data from a designed experiment in which the response is a curve. Recent advances in mixed nonlinear models and nonparametric regression with time series errors has made the use of mixed model self-modeli...

Semi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses

Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term's distribution is assumed to be Asymmetr...

Semi-parametric mixed effects models for longitudinal data with applications in business and economics

Longitudinal data is becoming increasingly common in business, social sciences, and biological sciences due to the advantages it offers over cross-section data in modeling and incorporating heterogeneity among subjects and in being able to make causal inferences from observational data. Parametric models and methods are widely used for analyzing longitudinal data for continuous, discrete, and c...

Semi-parametric Inference for Copula Models for Truncated Data

We investigate the dependent relationship between two failure time variables that truncate each other. Chaieb, Rivest, and Abdous (2006) proposed a semiparametric model under the so-called “semi-survival” Archimedean-copula assumption and discussed estimation of the association parameter, the truncation probability, and the marginal functions. Here the same model assumption is adopted but diffe...

Semi-parametric Inference for Copula Models for Dependently Truncated Data